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Expert Systems with Applications 28 (2005) 93–103
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Expert system methodologies and applications—a decade
review from 1995 to 2004
Shu-Hsien Liao*
Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Rd, Danshuei Jen, Taipei 251, Taiwan, ROC
Abstract
This paper surveys expert systems (ES) development using a literature review and classification of articles from 1995 to 2004 with a
keyword index and article abstract in order to explore how ES methodologies and applications have developed during this period. Based on
the scope of 166 articles from 78 academic journals (retrieved from five online database) of ES applications, this paper surveys and classifies
ES methodologies using the following eleven categories: rule-based systems, knowledge-based systems, neural networks, fuzzy ESs, objectoriented methodology, case-based reasoning, system architecture, intelligent agent systems, database methodology, modeling, and ontology
together with their applications for different research and problem domains. Discussion is presented, indicating the followings future
development directions for ES methodologies and applications: (1) ES methodologies are tending to develop towards expertise orientation
and ES applications development is a problem-oriented domain. (2) It is suggested that different social science methodologies, such as
psychology, cognitive science, and human behavior could implement ES as another kind of methodology. (3) The ability to continually
change and obtain new understanding is the driving power of ES methodologies, and should be the ES application of future works.
q 2004 Elsevier Ltd. All rights reserved.
Keywords: Expert systems; Expert system methodologies; Expert system applications; Literature survey
1. Introduction
Expert systems (ES) are a branch of applied artificial
intelligence (AI), and were developed by the AI community
in the mid-1960s. The basic idea behind ES is simply that
expertise, which is the vast body of task-specific knowledge,
is transferred from a human to a computer. This knowledge
is then stored in the computer and users call upon the
computer for specific advice as needed. The computer can
make inferences and arrive at a specific conclusion. Then
like a human consultant, it gives advices and explains, if
necessary, the logic behind the advice (Turban & Aronson,
2001). ES provide powerful and flexible means for
obtaining solutions to a variety of problems that often
cannot be dealt with by other, more traditional and orthodox
methods. Thus, their use is proliferating to many sectors of
our social and technological life, where their applications
* Tel.: C886-2947-2044; fax: C886-2945-3007.
E-mail address: [email protected].
0957-4174/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2004.08.003
are proving to be critical in the process of decision support
and problem solving.
As a part of ES research, this paper surveys the
development of ES through a literature review and
classification of articles from 1995 to 2004 as a basis,
exploring the ES methodologies and applications during
that period. The reason for choosing this period is that the
Internet was opened to general users in 1994 and this new
era of information and communication technology has
played important roles, not only in the field of ES, but also
in the ability to collect data from online database. This
literature survey started on March 2003 and it was based on
a search in the keyword index and article abstract for ‘ES’
on the Elsevier SDOS, IEEE Xplore, EBSCO (electronic
journal service), Ingenta, and Wiley InterScience online
database, for the period from 1995 to 2004, in which 10,439
articles were updated and found on June 2004. After topic
filtering, there were 166 articles from 78 journals related to
the keyword ‘ES applications’, 98 of which were connected
to the methodology of keyword ‘ES methodology’. Based
on the scope of 166 articles on ES applications, this paper
surveys and classifies ES methodologies using eleven
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S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
categories: rule-based systems, knowledge-based systems,
neural networks, fuzzy ESs, object-oriented methodology,
case-based reasoning (CBR), system architecture development, intelligent agent (IA) systems, modeling, ontology,
and database methodology together with their applications
for different research and problem domains.
The rest of the paper is organized as follows. Sections
2–12 present the survey results of ES methodologies and
applications based on the above categories, respectively.
Section 13 presents some discussion, extending to suggestions for future development of ES methodologies and
applications. Finally, Section 14 contains a brief conclusion.
2. Rule-based systems and their applications
A rule-based ES is defined as one, which contains
information obtained from a human expert, and represents
that information in the form of rules, such as IF–THEN. The
rule can then be used to perform operations on data to
inference in order to reach appropriate conclusion. These
inferences are essentially a computer program that provides
a methodology for reasoning about information in the rule
base or knowledge base, and for formulating conclusions.
Applications of rule-based systems on ESs are including:
state transition analysis, psychiatric treatment, production
planning, advisory system, teaching, electronic power
planning, automobile process planning, hypergraph representation, system development, knowledge verification/
validation, alcohol production, DNA histogram interpretation, knowledge base maintenance, scheduling strategy,
management fraud assessment, knowledge acquisition,
knowledge representation, communication system fault
diagnosis, bioseparation, material processing design,
resource utilization, biochemical nanotechnology, probabilistic fault diagnosis, agriculture planning, load scheduling, apiculture, tutoring system, geoscience, and sensor
control. The methodology of rule-based systems and their
applications are categorized in Table 1.
3. Knowledge-based systems and their applications
The most common definition of KBS is human-centered.
This highlights the fact that KBS have their roots in the field
of artificial intelligence (AI) and that they are attempts to
understand and initiate human knowledge in computer
systems (Wiig, 1994). The four main components of KBS
are usually distinguished as: a knowledge base, an inference
engine, a knowledge engineering tool, and a specific user
interface (Dhaliwal & Benbasat, 1996). On the other hand,
the term KBS includes all the organizational information
technology applications that may prove helpful to manage
the knowledge assets of an organization, such as ESs, rulebased systems, groupware, and database management
systems (DBMS) (Laudon & Laudon, 2002).
Table 1
Rule-based systems and their applications
Rule-based systems/applications
Authors
State transition analysis
Psychiatric treatment
Production planning
Advisory system
Teaching
Electronic power planning
Automobile process planning
Hypergraph representation
System development
Knowledge verification/validation
Alcohol production
DNA histogram interpretation
Ilgun, Kemmerer, and Porras (1995)
Goethe and Bronzino (1995)
Hamada, Baba, Sato, and Yufu (1995)
Kose et al. (1995)
Chan, Ma, Chan, and Chen (1995)
Rahman and Hazim (1996)
Sabourin and Villeneuve (1996)
Ramaswamy, Sarkar, and Chen (1997)
Mulvaney and Bristow (1997)
Wu and Lee (1997)
Knowledge base maintenance
Scheduling strategy
Management fraud assessment
Knowledge acquisition
Communication system fault
diagnosis
Bioseparation
Material processing design
Resource utilization
Biochemical nanotechnology
Probabilistic fault diagnosis
Agriculture planning
Load scheduling
Apiculture
Agricultural diagnostic/advisory
Geoscience
Sensor control
Tutoring system
Knowledge representation
Guerreiro et al. (1997)
Marchevsky, Truong, and Tolmachoff
(1997)
Higa and Lee (1998)
Zupan and Cheng (1998)
Deshmukh and Talluru (1998)
Wu and Chen (1999)
Leon, Mejias, Luque, and Gonzalo
(1999)
Lienqueo, Salgado, and Asenjo (1999)
Kim and Im (1999)
McCoy and Levary (2000)
Wasiewicz, Janczak, Mulawka, and
Plucienniczak (2000)
Leung and Romagnoli (2000)
Plant and Vayssieres (2000)
Croce et al. (2001)
Mahaman et al. (2002)
Mahaman, Passam, Sideridis, and
Yialouris (2003)
Soh, Tsatsoulis, Gineris, and Bertoia
(2004)
Valenzuela, Bentley, and Lorenz
(2004)
Hatzilygeroudis and Prentzas (2004)
Hatzilygeroudis and Prentzas (2004)
Some of these applications which are implemented by
knowledge-based systems include the following: medical
treatment, personal finance planning, engineering failure
analysis, waste management, production management,
thermal engineering, decision support, knowledge management, knowledge representation, power electronics design,
framed buildings evaluation, financial analysis, chemical
incident management, automatic tumor segmentation,
business game, climate forecasting, agricultural management, steel composition design, strategic management,
environmental protection, wastewater treatment, decision
making and learning, isokinetics interpretation, chemical
process controlling, therapy planning, plant process control,
outage locating planning, concurrent system design, case
validation, chip design, agriculture planning, power transmission protection, crop production planning, tropospheric
chemistry modeling, planar robots, and urban design. The
methodology of knowledge-based systems and their applications are categorized in Table 2.
S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
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Table 2
Knowledge-based systems and their applications
Table 3
Neural networks and their applications
Knowledge-based systems/
applications
Authors
Neural networks/applications
Authors
Fault diagnosis
Medical treatment
Alonso-Amo, Perez, Gomez, and
Montes (1995)
Dirks, Kingston, and Haggith (1995)
Graham-Jones and Mwllor (1995)
Wei and Weber (1996)
Dawood (1996)
Afgan and Carvalho (1996)
Keefe and Preece (1996)
Dutta (1997)
Mitra and Basu (1997)
Lu and Simmonds (1997)
Fezzani, Piquet, and Foch (1997)
Matsatsinis, Doumpos, and Zopounidis
(1997)
Finch and Lees (1997)
Clark et al. (1998)
Duan, Edwards, and Robins (1998)
Rodionov and Martin (1999)
Girard and Hubert (1999)
Manohar, Shivathaya, and Ferry (1999)
Volberda and Rutges (1999)
Gomolka and Orlowski (2000)
Baeza, Ferreira, and Laufuente (2000)
Mockler, Dologite, and Gartenfeld
(2000)
Alonso, Fuertes, Martinez, and Montes
(2000)
Barrera-Cortes, Astruc, and Tufeu
(2001)
Tunez, Aguila, and Marin (2001)
Acosta, Gonzalez, and Pulido (2001)
Liu and Schulz (2002)
Mills and Gomaa (2002)
Knauf, Gonzalez, and Abel (2002)
Bourbakis, Mogzadeh, Mertoguno, and
Koutsougeras (2002)
Cohen and Shoshany (2002)
Orduna, Garces, and Handschin (2003)
Edrees, Rafea, Fathy, and Yahia (2003)
Saunders, Pascoe, Johnson, Pilling, and
Jenkin (2003)
Xirogiannis, Stefanou, and Glykas
(2004)
Sen, Minambres, Garrido, Almansa,
and Soto (2004)
Wang, Qu, Liu, and Cheng (2004),
Yang, Han, and Kim, (2004)
Personal finance planning
Engineering failure analysis
Waste management
Production management
Thermal engineering
Decision support
Knowledge management
Knowledge representation
Framed buildings evaluation
Power electronics design
Financial analysis
Chemical incident management
Automatic tumor segmentation
Business game
Climate forecasting
Agricultural management
Steel composition design
Strategic management
Environmental protection
Wastewater treatment
Decision making and learning
Isokinetics interpretation
Chemical process controlling
Physical therapy planning
Plant process control
Outage locating planning
Concurrent system design
Case validation
Chip design
Agricultural planning
Power transmission protection
Crop production planning
Tropospheric chemistry modeling
Urban design
Planar robots
4. Neural networks and their applications
An artificial neural network (ANN) is a model that
emulates a biological neural network. This concept is used
to implement software simulations for the massively
parallel processes that involve processing elements interconnected in network architecture. The artificial neuron
receives inputs that are analogous to the electrochemical
impulses that the dendrites of biological neurons receive
from other neurons. The output of the artificial neuron
corresponds to signals sent out from a biological neuron
Optimal power flow
Decision making
Alarm processing system
Inference mechanisms
Diagnostic system
Machine learning
Power load forecasting
Facility layout design
Process control
Knowledge learning
Gold mining process design
Robotic systems
Parameter setting
Waste treatment
Biomedical application
Mitigation processes control
Engineering ceramics
Acoustic signal diagnosing
Crude oil distillation
Fu (1998)
Li, Tasi, Tasi, and Chiu (2004)
Liau et al. (2004)
over its axon. These artificial signals can be changed
similarly to the physical changes occurring at neural
synapses (Turban & Aronson, 2001).
Some of the applications that are implemented by neural
networks are the following: fault diagnosis, optimal power
flow, decision making, alarm processing system, inference
mechanisms, diagnostic system, machine learning, power
load forecasting, facility layout design, process control,
knowledge learning, gold mining process design, robotic
systems, parameter setting, waste treatment, engineering
ceramics, mitigation processes control, acoustic signal
diagnosing, crude oil distillation, and biomedical application. The methodology of neural networks and their
applications are categorized in Table 3.
5. Fuzzy expert systems and their applications
Fuzzy ESs are developed using the method of fuzzy
logic, which deals with uncertainty. This technique, which
uses the mathematical theory of fuzzy sets, simulates the
process of normal human reasoning by allowing the
computer to behave less precisely and logically than
conventional computers. This approach is used because
decision-making is not always a matter of black and white,
true or false; it often involves gray areas and the term may
be. Accordingly, creative decision-making processes can be
characterized unstructured, playful, contentious, and rambling (Jamshidi, Titli, Zadeh, & Boverie 1997).
Some applications implemented by fuzzy ESs are such
as: power load forecasting, online scheduling, chemical
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Table 4
Fuzzy expert systems and their applications
Table 5
Object-oriented methodology and their applications
Fuzzy expert systems/
applications
Authors
Object-oriented methodology/
applications
Authors
Power load forecasting
Online scheduling
Chemical process fault diagnosis
Ecological planning
Power system diagnosis
Control systems
Uncertainly reasoning
Knowledge integration
Fault diagnosis
Kim, Park, Hwang, and Kim (1995)
Chang and Thia (1996)
Ozyurt and Kandel (1996)
Zhu, Band, Dutton, and Nimlos (1996)
Cho and Park (1997)
Bugarin and Barro (1998)
Pan, DeSouza, and Kak (1998)
Lee, Han, Song, and Lee (1998)
Lee et al. (2000) and Soliman, Rizzoni,
and Kim (1999)
Dash, Mishra, Salama, and Liew
(2000)
EI-Shal and Morris (2000)
Benson and Asgarpoor (2000)
Carrasco, Rodriguez, Punal, Roca, and
Lema (2004), Punal, Rodriguez, Carrasco, Roca, and Lema (2002), and
Punal et al. (2001)
Wong and Hamouda (2002, 2003)
Mahabir, Hicks, and Fayek (2003)
Liao (2003)
Leung, Lau, and Kwong (2003)
Ngai and Wat (2003)
Lababidi and Baker (2003)
Meesad and Yen (2003) and Sendelj
and Devedzic (2004)
Shu and Burn (2004)
Boegl, Adlassnig, Hayashi, Rothenfluh,
and Leitich (2004)
Drigs et al. (2004)
Padilla-Medina and Sanchez-Marin
(2004)
Reznik and Dabke (2004)
Frantti and Kallio (2004)
Industry diagnosis
Knowledge representation
Electronic power capacity planning
Knowledge learning
Power system maintenance
Batanov and Cheng (1995)
Vranes and Stanojevic (1995)
Deb (1995)
Menzies (1997)
Kawahara, Sasaki, Kubokawa,
Asahara, and Sugiyama (1998)
Geymayr and Ebecken (1998)
Lau, Tso, and Ho (1998)
Depradine (2003)
Power system classification
Fault detection
Demand evaluation
Wastewater treatment
Machinability data selection
Water supply forecast
Radiography classification
On-line analytic processing
Hotel selection
Dryer tool integration
Medical diagnosis
Pooled flood frequency analysis
Medical consultation system
Job matching
Performance indexing
Computer security
Gesture recognition
process fault diagnosis, ecological planning, control systems, uncertainly reasoning, knowledge integration, fault
diagnosis, power system classification, fault detection,
demand evaluation, wastewater treatment, machinability
data selection, water supply forecast, radiography classification, on-line analytic processing, hotel selection, dryer
tool integration, pooled flood frequency analysis, medical
consultation system, job matching, performance indexing,
computer security, gesture recognition, and medical diagnosis. The methodology of fuzzy ESs together with their
applications is categorized in Table 4.
6. Object-oriented methodology and their applications
Object-oriented methodology combines into one object
data together with the specific procedures that operate on
this data, where the object combines data and program code.
Instead of passing data to procedures, programs send a
message for an object to perform a procedure that is already
embedded in it. Then, the same message may be sent to
Knowledge engineering
Manufacturing information network
Syntactic programming
many different objects, but each will implement that
message differently. An object’s data are encapsulated
from other parts of the system, so each object is an
independent software building block that can be used in
many different systems without changing the program code.
Some applications implemented by object-oriented
methodology include the following: industry diagnosis,
knowledge learning, manufacturing information network,
power system maintenance, knowledge engineering, syntactic programming, and knowledge representation. The
methodology of object-oriented methodology and their
applications are categorized in Table 5.
7. Case-based reasoning and their applications
The basic idea of CBR is to adapt solutions that were
used to solve previous problems and use them to solve new
problems. In CBR, descriptions of past experience of human
specialists, represented as cases, are stored in a database for
later retrieval when the user encounters a new case with
similar parameters. The system searches for stored cases
with problem characteristics similar to the new one, finds
the closest fit, and applies the solutions of the old case to the
new case. Successful solutions are tagged to the new case
and both are stored together with the other cases in the
knowledge base. Unsuccessful solutions also are appended
to the case base along with explanations as to why the
solutions did not work (Kolonder, 1994).
Some of the applications implemented by CBR include
the following: manufacturing process design, knowledge
management, power system restoration training, ultrasonic
inspection, medical planning, medical application, fault
diagnosis, e-learning, and knowledge modeling. These CBR
and their applications are categorized in Table 6.
8. Modeling and their applications
Modeling methodology becomes an interdisciplinary
methodology of ES in order to build formal relationships
with logical model design in different knowledge/problem
S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
97
Table 6
Case-based reasoning and their applications
Table 8
System architecture and their applications
Case-based reasoning/applications
Authors
System architecture/applications
Authors
Manufacturing process design
Takahashi, Oono, and Saitog
(1995)
Noh, Lee, Kim, Lee, and Kim
(2000)
Islam and Chowdhury (2001)
Jarmulak, Kerckhoffs, and Veen
(2001)
Abidi and Manickam (2002)
Montani and Bellazzi (2002)
Gardan and Gardan (2003)
Yang et al. (2004)
Fu and Shen (2004)
Material evaluation and selection
Computer aided design
Mahmoud and AL-Hammad (1996)
Tucho, Sierra, Fernandez, Vijande,
and Moris (2003) and Vranes and
Stanojevic (1999)
Gilad and Karni (1999)
Khan and Hafiz (1999)
Collier, Leech, and Clark (1999)
Knowledge management
Power system restoration training
Ultrasonic inspection
Medical planning
Medical application
Knowledge modeling
Fault diagnosis
E-learning
Ergonomics design
ISO system implementation
Corporate recovery decision support
Concurrent engineering
Military application
Training simulator
Ferryboat configuration
Liquid retaining structure design
Reidsema and Szczerbicki (2001)
Liao (2001)
Lopez, Flores, and Garcia (2003)
Shaalan, Rizk, Abdelhamid, and
Bahgat (2004)
Chau and Albermani (2004)
domains. Furthermore, modeling technology can provide
quantitative methods to analyze data to represent or acquire
expert knowledge with inductive logic programming or
algorithms so that AI, cognitive science and other research
fields could have broader platforms to implement technologies for ES development.
The applications implemented by modeling are such as:
process control, medical analysis, management decisionmaking, software evaluation, medical system validation,
assembly task planning and simulation, transport terminal
design, project allocation, and endometrial hyperplasia
classification. The methodology of modeling and their
applications are categorized in Table 7.
architecture design and implementation are completed,
users can manipulate and control system functions on the
system architecture.
Some of the applications implemented by system
architecture illustrate the following: material evaluation
and selection, computer aided design, ergonomics design,
ISO system implementation, corporate recovery decision
support, concurrent engineering, military application, training simulator, liquid retaining structure design, and ferryboat configuration. These System architectures and their
applications are categorized in Table 8.
9. System architecture and their applications
10. Intelligent agents and their applications
System architecture of an ES is similar to an architectural
sketch of a house. It gives users a general idea of what the
system will look like and how it is going to implement
systems. The architecture shows the general capabilities of
the system, the users’ interfaces, system functions, system
(data) flow, system management, DBMS, necessary protocol, and specific programming language, such as blackboard
architecture, CommonKADS, etc. Once the system
An IA is a computer program that helps a user with
routine computer tasks. This is a new technology, and as
such there are several definitions, database capabilities, and
different applications in autonomous programs. Several of
the names used to describe IAs are include software agents,
wizards, and multi-agent (Turban & Aronson, 2001).
Some of the applications implemented by IAs are such
as: tutoring systems, system analysis and design, electronic
service maintenance, carbon contamination rules, knowledge representation, adaptive systems, air pollution control,
building architecture design, agricultural decision support,
industry simulation, and knowledge engineering on the
WWW platform. The methodology of IAs together with
their applications is categorized in Table 9.
Table 7
Modeling and their applications
Modeling/applications
Process control
Medical analysis
Management decision-making
Software evaluation
Medical system validation
Assembly task planning and
simulation
Endometrial hyperplasia classification
Transport terminal design
Project allocation
Authors
Peng, Xiao, Nie, Wang, and Wang
(1996)
Mookerjee and Mannino (1997)
Vlahavas, Stamelos, Refanidis, and
Tsoukias (1999)
Martin-Baramera et al. (2000)
Zha and Lim (2000)
Morrison et al. (2002)
Abacoumkin and Ballis (2004)
Cheung, Hong, and Ang (2004)
11. Ontology and their applications
Ontology is a system of vocabulary, which is used as a
fundamental concept for describing the task/domain knowledge to be identified. This vocabulary is used as a
communication basis between domain experts and knowledge engineers. Accordingly, a reusable task/domain model
can be represented and a computer program code is
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S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
Table 9
Intelligent agents and their applications
Table 11
Database methodology and their applications
Intelligent agents/applications
Authors
Authors
Tutoring systems
Supply chain
Cruces and Arriaga (2000)
Gjerdrum, Shah, and Papageorgiou
(2001)
Gruer, Hilaire, Koukam, and Cetnarowicz (2002)
Yu, Iung, and Panetto (2003)
Vegh (2003)
Qian, Li, Jiang, and Wen (2003)
Aldea et al. (2004)
Juuso (2004)
Zhou, Huang, and Chan (2004)
Alibaba and Ozdeniz (2004)
Bonstre et al. (2004)
Thomson and Willoughby (2004)
Shaalan, EI-Badry, and Rafea
(2004)
Database methodology/
applications
Power system planning
Geography planning
Geographical information
system
Sedimentary rock interpretation
Park and Lee (1995)
Kirkby (1996)
Filis, Sabrakos, Yialouris, Sideridis,
and Mahaman (2003)
Abel, Silva, Ros, Mastella, and Campbell (2004)
Wang et al. (2004)
System analysis and design
Electronic service maintenance
Carbon contamination rules
Knowledge representation
Industry simulation
Adaptive systems
Air pollution control
Building architecture design
Olive oil automation
Agricultural decision support
Knowledge engineering on the
WWW platform
generated in that ontology for knowledge acquisition, reuse,
heuristic learning.
Some applications implemented by ontology include the
following: medical decision support, knowledge reuse,
preventive control, landscape assessment, knowledge
acquisition, and chess heuristic pruning. These ontology
and their applications are categorized in Table 10.
Traditional Chinese medicine
diagnosis
Medical expert system
Yan et al. (2004)
knowledge from large databases, such as data mining and
searching approach.
Some applications implemented by database methodology present as the following: power system planning,
geography planning, geographical information system,
sedimentary rock interpretation, traditional Chinese medicine diagnosis, and medical ES. The methodology of
database methodology and their applications are categorized in Table 11.
13. Discussions, limitations, and suggestions
13.1. Discussions
12. Database methodology and their applications
A database is a collection of data organized to efficiently
serve many applications by centralizing the data and
minimizing redundant data (McFadden, Hoffer, & Prescott,
2000). A DBMS is the software that permits an organization
to centralize data, manage them efficiently, and provide
access to the stored data by application programs (Laudon &
Laudon, 2002). However, some large databases make
knowledge discovery computationally expensive because
some domains or background knowledge, hidden in the
database may guide and restrict the search for important
knowledge. Therefore, modern database methodologies
need to process large volumes, multiple hierarchies, and
different data formats to discover in-depth expert
Table 10
Ontology and their applications
Ontology/applications
Authors
Medical decision support
Tu, Eriksson, Gennari, Shahar, and Musen
(1995)
Takaoka and Mizoguchi (1996)
Thukaram and Parthasarathy (1997)
Martinez-Bejar, Ibanez-Cruz, Compton, and
Cao (2001)
Ruiz-Sanchez, Valencia-Garcia, FernandezBreis, Martinez-Bejar, and Compton (2003)
Montani and Bellazzi (2002)
Gardan and Gardan 2(003)
Knowledge reuse
Preventive control
Landscape assessment
Knowledge acquisition
Chess heuristic pruning
Knowledge modelling
ES methodologies and applications are a broad category
of research issues on ES Some specific methodologies and
methods are presented as examples for exploring the
suggestions and solutions to specific ES problem domains.
Therefore, methodologies and applications of ES are
attracting much attention and efforts, both academic and
practical. From this literature review, we can see that ES
methodologies and applications developments are diversified due to their authors’ backgrounds, expertise, and
problem domains. This is why some authors can appear in
the literature on different methodologies and applications.
On the other hand, some methodologies have common
concepts, and types of methodology. For example, rulebased systems and knowledge-based systems, or fuzzy logic
versus ANN methodology. However, a few authors work in
different methodologies and applications. This indicates that
the trend of development on methodology is also diversified
due to author’s research interests and abilities in the
methodology and problem domain. This may indicate that
the development of ES methodologies is directed toward
expertise orientation.
Furthermore, some applications have a high degree of
overlap in different methodologies. For example,
teaching/ training, knowledge acquisition, knowledge
representation, knowledge learning, fault diagnosis/detection, medical applications, production planning, system
design/development, modeling, process control, decision
S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
making, waste treatment, resource management, biomedical
application, robotic systems, forecasting, ecological planning, agriculture planning, geoscience, power system
planning, chemical application, industry planning, management issues, and knowledge reuse, are all topics of different
methodologies, which implement ES in a common problem
domain. This indicates that those applications are the major
trend of ES development, and many methodologies focus on
these problems. This may direct development of ES
applications toward problem domain orientation.
In this paper, most of the articles discussed were from
different categories including agriculture, agronomy, automation, biochemistry, biology, chemistry, computer
science, biology, ecology, education, energy, engineering,
entomology, environmental sciences, genetics, geochemistry, geology, geosciences, health care sciences, hematology,
hydrology, materials, mathematics, mechanics, medical,
military, operation research/management sciences, ontology, plant science, remote sensing, robotics, and water
resources, which retrieved from Elsevier SDOS, IEEE
Xplore, EBSCO (electronic journal service), Ingenta, and
Wiley InterScience online database. We do not conclude
that ES methodologies and applications are not developed in
other science fields. However, we would like to see more ES
methodologies and applications of different research fields
published in order to broaden our horizon of academic and
practice works on ES.
13.2. Limitations
Firstly, a literature review for the broad category of ES
methodologies and applications is a difficult task due to the
extensive background knowledge needed for collecting,
studying, and classifying these articles. Although limited in
background knowledge, this paper makes a brief literature
review of ES from 1995 to 2004 in order to explore how ES
methodologies and applications have developed in this
period. Indeed, the categorization of methodologies and
their applications is based on the keyword index and article
abstract in this research. Some other articles may have
implemented similar ES methodologies in their applications
without an ES index, so this paper might not find these
reference sources. Therefore, the first limitation of this
article is the author’s limited knowledge in presenting an
overall picture of this subject.
Secondly, although 166 articles from 78 academic
journals (five online databases) cited in this paper, there
are other academic journals listed in the science citation
index (SCI) engineering index (EI), and the social science
citation index (SSCI), as well as other academic journals/
magazines, practical articles and reports are not included in
this survey. These would have provided more complete
information to explore the development of ES methodologies and applications.
Thirdly, non-English publications are not considered in
this survey to determine the effects of different cultures on
99
the development of ES methodologies and applications. We
believe that ES methodologies and applications in addition
to those discussed in this article have also been developed
and published in other areas and languages.
13.3. Suggestions
(1) Other social science methodologies. In this article, the
definition of ES methodology is not complete because
other methodologies, such as social science methodologies, were not included in the survey. However,
qualitative questionnaires and statistical methods are
another research technology to solve problems in social
studies. For example, cognitive science, psychology and
human behavior are used to implement different
methods for exploring specific human expert problem.
Therefore, other social sciences methodologies may
include ES methodology categories in future works.
(2) Integration of methodologies. ES is an interdisciplinary
research topic. Thus, future ES developments need
integration with different methodologies, and this
integration of methodologies and cross-interdisciplinary research may offer more technologies to investigate
ES problems.
(3) Change is a source of future ES development. The
change due to social and technical reasons may either
enable or inhibit ES methodologies and application
development. This means that inertia, stemming from
the use of routine problem solving procedures, stagnant
knowledge sources, and following past experience or
knowledge may impede changes in terms of learning
and innovation for individuals and organizations.
Therefore, to continue creating, sharing, learning, and
storing knowledge on different methodologies and
application domains may also become a source of ES
development.
14. Conclusions
This paper is based on a literature review of ES
methodologies and applications from 1995 to 2004 using a
keyword index and article title search. We conclude that ES
methodologies are tending to develop towards expertise
orientation and that ES applications development is a
problem-oriented domain. It is suggested that different
social science methodologies, such as psychology, cognitive
science, and human behavior could implement ES as
another kind of methodology. Integration of qualitative,
quantitative and scientific methods and integration of ES
methodologies studies may broaden our horizon on this
subject. Finally, the ability to continually change and obtain
new understanding is the power of ES methodologies, and
will be the ES application of future works.
100
S.-H. Liao / Expert Systems with Applications 28 (2005) 93–103
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